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    • 25. 发明授权
    • System and methods for content-based financial decision making support
    • 基于内容的财务决策支持系统和方法
    • US08930247B1
    • 2015-01-06
    • US12023934
    • 2008-01-31
    • Xiaoping ZhangDavid KedmeyFang Wang
    • Xiaoping ZhangDavid KedmeyFang Wang
    • G06Q30/00G06Q10/06G06F17/18
    • G06Q10/0639G06F17/18G06Q40/04G06Q40/06
    • Robust content-based decision-making support is enabled by software with a customizable knowledge base. Utilizing proprietary information contained within a knowledge base, the software enables users to search the indexed database by feature, example firm, or pattern and update the knowledge base based on the results. The information contained in the knowledge base enables results to be ranked by relevance and enables other feedback to be provided. The system and methods provide process support by helping financial professionals identify, analyze, and construct data analysis patterns based on individual domain knowledge and preferences. The system and methods automatically detect abnormal patterns and automatically analyze their correlations to market events to provide further process support to financial professionals. Using the results of any searching, analysis, and processing, the system and methods provide a neural network or other learning algorithm to provide content-based decision-making support.
    • 基于内容的稳定的决策支持由具有可定制知识库的软件实现。 利用知识库中包含的专有信息,该软件使用户能够通过特征,示例公司或模式搜索索引数据库,并根据结果更新知识库。 知识库中包含的信息可使结果按照相关性进行排名,并提供其他反馈信息。 系统和方法通过帮助金融专业人员根据个人领域知识和偏好来识别,分析和构建数据分析模式来提供流程支持。 系统和方法自动检测异常模式,并自动分析其与市场事件的相关性,为财务专业人员提供进一步的流程支持。 使用任何搜索,分析和处理的结果,系统和方法提供神经网络或其他学习算法来提供基于内容的决策支持。
    • 29. 发明授权
    • System and method for video summarization and navigation based on statistical models
    • 基于统计模型的视频摘要和导航系统和方法
    • US09384780B2
    • 2016-07-05
    • US14246869
    • 2014-04-07
    • Xiaoping ZhangJunfeng Jiang
    • Xiaoping ZhangJunfeng Jiang
    • H04N5/783H04N9/80G11B27/00H04N5/14H04N5/93H04N21/44H04N21/472H04N21/845G06F17/30H04N9/82
    • G11B27/005G06F17/30846H04N5/147H04N5/783H04N5/93H04N9/8227H04N21/44008H04N21/47217H04N21/8456
    • The disclosed method calculates video time density functions based on inter-frame mutual information or other similarity measures. The method includes acquiring a video sequence from memory, computing mutual information or other similarity measures between two consecutive frames of the video sequence, and constructing a video temporal density function based on the mutual information or similarity measures. The method enables fast navigation of the video sequence by performing a temporal quantization of the video time density function to determine a set of quanta. The video sequence can be navigated using the nearest neighbor video frames to the set of quanta. The method enables thumbnail extraction of a video sequence using statistical modeling by performing a temporal quantization of the video time density function to segment video sequence in time domain and using mixture (such as Gaussian mixture and ICA mixture) vector quantization to find the sample frames for each temporal segment.
    • 所公开的方法基于帧间相互信息或其他相似性度量来计算视频时间密度函数。 该方法包括从存储器获取视频序列,计算视频序列的两个连续帧之间的相互信息或其他相似性度量,以及基于相互信息或相似性度量构建视频时间密度函数。 该方法通过执行视频时间密度函数的时间量化来确定一组量子,从而能够快速导航视频序列。 视频序列可以使用最近邻视频帧导航到量子集。 该方法通过使用视频时间密度函数的时间量化来在时域中分割视频序列并使用混合(诸如高斯混合和ICA混合)矢量量化来使用统计建模缩略图提取视频序列,以找到用于 每个时间段。
    • 30. 发明申请
    • SYSTEM AND METHOD FOR USING DATA INCIDENT BASED MODELING AND PREDICTION
    • 使用基于数据事件的建模和预测的系统和方法
    • US20160019218A1
    • 2016-01-21
    • US14750669
    • 2015-06-25
    • Xiaoping ZhangDavid KedmeyFang Wang
    • Xiaoping ZhangDavid KedmeyFang Wang
    • G06F17/30
    • G06F16/2457G06F16/2428G06F2216/03G06Q40/04
    • A system and method for enabling information extraction from large data sets (so-called “big data”) according to a new paradigm is disclosed. This system does not generate functions describing why certain inputs result in certain outputs. Instead, it creates incident mappings of inputs to outputs without regard to why inputs result in outputs. These mappings can be distributions or other data sets representative of different outcomes occurring. This enables several useful operations. For example, by providing a data set indicative of outputs that have historically occurred following a particular input, the disclosed system can be used to predict future outcomes with probabilities. For example, if a particular stock price pattern is provided as an input, the system generates an output data set indicating the probabilities of certain price behaviors following that input pattern. This data set can thus be used to predict future behavior. Other useful operations are disclosed herein.
    • 公开了一种用于根据新范例从大数据集(所谓的“大数据”)提取信息的系统和方法。 该系统不会产生描述为什么某些输入导致某些输出的功能。 相反,它会创建对输出的输入的事件映射,而不考虑为什么输入会导致输出。 这些映射可以是代表不同结果发生的分布或其他数据集。 这样可以进行几个有用的操作。 例如,通过提供指示在特定输入之后历史地发生的输出的数据集,所公开的系统可以用于以概率来预测未来的结果。 例如,如果提供特定股票价格模式作为输入,则系统生成指示在该输入模式之后的某些价格行为的概率的输出数据集。 因此,该数据集可用于预测将来的行为。 本文公开了其它有用的操作。